Mockingbird

Introduction

Linguist's Classifier in Go.

Linguist can be used as a Go package by

import "github.com/lazywei/linguist"

and it also has a CLI (command line interface) in cli/

$ cd cli/
$ ./build.sh
$ ./mockingbird --help

Command Line Interface Usage

Preparing LIBSVM format dataset

Collect Rosetta Code

1. Clone the RosettaCodeData

git clone [email protected]:acmeism/RosettaCodeData.git

2. Build this cli executable

cd cli/
./build.sh

3. Run the collectRosetta according to the cloned RosettaCodeData, and collect files to ../samples

./mockingbird collectRosetta path/to/clones/RosettaCodeData ../samples

Build Bag-of-Words and Convert Samples to Libsvm

Build from scratch

./mockingbird convertLibsvm ../samples ../

This will save libsvm.samples and bow.gob to ../. The bow.gob is the parameters for constructing bag-of-words. This can be used afterward:

./mockingbird convertLibsvm ../samples ../ --bowPath ../bow.gob

Train and Predict

Train

For example, train a logisitic regression classifier:

./mockingbird train --sample=./test_fixture/test_samples.libsvm --solver 1

This will save a model file in $PWD/model/lr.model, which can be used in later prediction.

Full usage:

usage: mockingbird train [<flags>]

Train Classifier

Flags:
  --help            Show help (also see --help-long and --help-man).
  --sample="samples.libsvm"
                    Path for samples (in libsvm format)
  --output="model"  Path for saving trained model
  --solver=0        0 = NaiveBayes, 1 = LogisticRegression

Predict

For example, make prediction via previously trained logisitic regression classifier:

./mockingbird predict --model=./model/lr.model --data=./test_fixture/test_samples.libsvm --solver=1

Full usage:

usage: mockingbird predict --data=DATA [<flags>]

Predict via trained Classifier

Flags:
  --help       Show help (also see --help-long and --help-man).
  --model="./model/naive_bayes.gob"
               Path for loading saved model
  --data=DATA  Path for testing data (in libsvm format)
  --solver=0   0 = NaiveBayes, 1 = LogisticRegression